DSP Implementation of a Neural Network Vector Controller for IPM Motor Drives

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ID: 50462
2019
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Abstract
This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions.
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sun2019dspenergies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yang Sun, Shuhui Li, Malek Ramezani, Bharat Balasubramanian, Bian Jin, Yixiang Gao
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Journal energies
Year 2019
DOI
DOI not found
URL
https://www.mdpi.com/1996-1073/12/13/2558
Keywords
Technology (General) Engineering (General). Civil engineering (General) Information technology Technology Medical technology automation special aspects of education geology

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